709e121cd5
[NFC] polish applications/Chat/coati/models/generation.py code style ( #4275 )
2023-07-26 14:12:57 +08:00
77c469e1ba
[NFC] polish applications/Chat/coati/models/base/actor.py code style ( #4248 )
2023-07-26 14:12:57 +08:00
3d8d5d0d58
[chat] use official transformers and fix some issues ( #4117 )
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* feat: remove on_learn_epoch fn as not used
* revert: add _on_learn_epoch fn
* feat: remove NaiveStrategy
* test: update train_prompts tests
* fix: remove prepare_llama_tokenizer_and_embedding
* test: add lora arg
* feat: remove roberta support in train_prompts due to runtime errs
* feat: remove deberta & roberta in rm as not used
* test: remove deberta and roberta tests
* feat: remove deberta and roberta models as not used
* fix: remove calls to roberta
* fix: remove prepare_llama_tokenizer_and_embedding
* chore: update transformers version
* docs: update transformers version
* fix: fix actor inference
* fix: fix ci
* feat: change llama pad token to unk
* revert: revert ddp setup_distributed
* fix: change llama pad token to unk
* revert: undo unnecessary changes
* fix: use pip to install transformers
2023-07-04 13:49:09 +08:00
9d02590c9a
[chat] refactor actor class ( #3968 )
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* refactor: separate log_probs fn from Actor forward fn
* refactor: separate generate fn from Actor class
* feat: update unwrap_model and get_base_model
* unwrap_model returns model not wrapped by Strategy
* get_base_model returns HF model for Actor, Critic and RewardModel
* feat: simplify Strategy.prepare
* style: remove get_base_model method of Actor
* perf: tokenize text in batches
* refactor: move calc_action_log_probs to utils of model
* test: update test with new forward fn
* style: rename forward fn args
* fix: do not unwrap model in save_model fn of naive strategy
* test: add gemini test for train_prompts
* fix: fix _set_default_generate_kwargs
2023-06-13 13:31:56 +08:00
b5f0566363
[chat] add distributed PPO trainer ( #3740 )
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* Detached ppo (#9 )
* run the base
* working on dist ppo
* sync
* detached trainer
* update detached trainer. no maker update function
* facing init problem
* 1 maker 1 trainer detached run. but no model update
* facing cuda problem
* fix save functions
* verified maker update
* nothing
* add ignore
* analyize loss issue
* remove some debug codes
* facing 2m1t stuck issue
* 2m1t verified
* do not use torchrun
* working on 2m2t
* working on 2m2t
* initialize strategy in ray actor env
* facing actor's init order issue
* facing ddp model update issue (need unwarp ddp)
* unwrap ddp actor
* checking 1m2t stuck problem
* nothing
* set timeout for trainer choosing. It solves the stuck problem!
* delete some debug output
* rename to sync with upstream
* rename to sync with upstream
* coati rename
* nothing
* I am going to detach the replaybuffer from trainer and make it a Ray Actor. Two benefits: 1. support TP trainer. 2. asynchronized buffer operations
* experience_maker_holder performs target-revolving _send_experience() instead of length comparison.
* move code to ray subfolder
* working on pipeline inference
* apply comments
* working on pipeline strategy. in progress.
* remove pipeline code. clean this branch
* update remote parameters by state_dict. no test
* nothing
* state_dict sharding transfer
* merge debug branch
* gemini _unwrap_model fix
* simplify code
* simplify code & fix LoRALinear AttributeError
* critic unwrapped state_dict
---------
Co-authored-by: csric <richcsr256@gmail.com>
* [chat] add perfomance evaluator and fix bugs (#10 )
* [chat] add performance evaluator for ray
* [chat] refactor debug arg
* [chat] support hf config
* [chat] fix generation
* [chat] add 1mmt dummy example
* [chat] fix gemini ckpt
* split experience to send (#11 )
Co-authored-by: csric <richcsr256@gmail.com>
* [chat] refactor trainer and maker (#12 )
* [chat] refactor experience maker holder
* [chat] refactor model init
* [chat] refactor trainer args
* [chat] refactor model init
* [chat] refactor trainer
* [chat] refactor experience sending logic and training loop args (#13 )
* [chat] refactor experience send logic
* [chat] refactor trainer
* [chat] refactor trainer
* [chat] refactor experience maker
* [chat] refactor pbar
* [chat] refactor example folder (#14 )
* [chat] support quant (#15 )
* [chat] add quant
* [chat] add quant example
* prompt example (#16 )
* prompt example
* prompt load csv data
* remove legacy try
---------
Co-authored-by: csric <richcsr256@gmail.com>
* [chat] add mmmt dummy example and refactor experience sending (#17 )
* [chat] add mmmt dummy example
* [chat] refactor naive strategy
* [chat] fix struck problem
* [chat] fix naive strategy
* [chat] optimize experience maker sending logic
* [chat] refactor sending assignment
* [chat] refactor performance evaluator (#18 )
* Prompt Example & requires_grad state_dict & sharding state_dict (#19 )
* prompt example
* prompt load csv data
* remove legacy try
* maker models require_grad set to False
* working on zero redundancy update
* mmmt_prompt example; naive strategy requires_grad state_dict & sharding; maker model requires_no_grad.
* remove legacy examples
* remove legacy examples
* remove replay buffer tp state. bad design
---------
Co-authored-by: csric <richcsr256@gmail.com>
* state_dict sending adapts to new unwrap function (#20 )
* prompt example
* prompt load csv data
* remove legacy try
* maker models require_grad set to False
* working on zero redundancy update
* mmmt_prompt example; naive strategy requires_grad state_dict & sharding; maker model requires_no_grad.
* remove legacy examples
* remove legacy examples
* remove replay buffer tp state. bad design
* opt benchmark
* better script
* nothing
* [chat] strategy refactor unwrap model
* [chat] strategy refactor save model
* [chat] add docstr
* [chat] refactor trainer save model
* [chat] fix strategy typing
* [chat] refactor trainer save model
* [chat] update readme
* [chat] fix unit test
* working on lora reconstruction
* state_dict sending adapts to new unwrap function
* remove comments
---------
Co-authored-by: csric <richcsr256@gmail.com>
Co-authored-by: ver217 <lhx0217@gmail.com>
* [chat-ray] add readme (#21 )
* add readme
* transparent graph
* add note background
---------
Co-authored-by: csric <richcsr256@gmail.com>
* [chat] get images from url (#22 )
* Refactor/chat ray (#23 )
* [chat] lora add todo
* [chat] remove unused pipeline strategy
* [chat] refactor example structure
* [chat] setup ci for ray
* [chat-ray] Support LoRA trainer. LoRA weights reconstruction. (#24 )
* lora support prototype
* lora support
* 1mmt lora & remove useless code
---------
Co-authored-by: csric <richcsr256@gmail.com>
* [chat] fix test ci for ray
* [chat] fix test ci requirements for ray
* [chat] fix ray runtime env
* [chat] fix ray runtime env
* [chat] fix example ci docker args
* [chat] add debug info in trainer
* [chat] add nccl debug info
* [chat] skip ray test
* [doc] fix typo
---------
Co-authored-by: csric <59389055+CsRic@users.noreply.github.com>
Co-authored-by: csric <richcsr256@gmail.com>
2023-06-07 10:41:16 +08:00
842768a174
[chat] refactor model save/load logic ( #3654 )
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* [chat] strategy refactor unwrap model
* [chat] strategy refactor save model
* [chat] add docstr
* [chat] refactor trainer save model
* [chat] fix strategy typing
* [chat] refactor trainer save model
* [chat] update readme
* [chat] fix unit test
2023-04-27 18:41:49 +08:00
6ef7011462
[chat] remove lm model class ( #3653 )
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* [chat] refactor lora
* [chat] remove lm class
* [chat] refactor save model
* [chat] refactor train sft
* [chat] fix ci
* [chat] fix ci
2023-04-27 15:37:38 +08:00
50793b35f4
[gemini] accelerate inference ( #3641 )
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* [gemini] support don't scatter after inference
* [chat] update colossalai strategy
* [chat] fix opt benchmark
* [chat] update opt benchmark
* [gemini] optimize inference
* [test] add gemini inference test
* [chat] fix unit test ci
* [chat] fix ci
* [chat] fix ci
* [chat] skip checkpoint test
2023-04-26 16:32:40 +08:00
d7bf284706
[chat] polish code note typo ( #3612 )
2023-04-20 17:22:15 +08:00
36a519b49f
Update test_ci.sh
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update
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Update run_chatgpt_examples.yml
Update run_chatgpt_examples.yml
Update run_chatgpt_examples.yml
Update run_chatgpt_examples.yml
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Update run_chatgpt_examples.yml
Update test_ci.sh
Update test_ci.sh
update
Update run_chatgpt_examples.yml
Update run_chatgpt_examples.yml
update ci
Update test_ci.sh
Update run_chatgpt_examples.yml
Update run_chatgpt_examples.yml
Update run_chatgpt_examples.yml
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Update run_chatgpt_examples.yml
Update run_chatgpt_examples.yml
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Update test_ci.sh
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update test ci
RoBERTa for RLHF Stage 2 & 3 (still in testing)
Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)"
This reverts commit 06741d894d06741d894d
2023-04-18 14:33:12 +08:00
e6a132a449
[chat]: add vf_coef argument for PPOTrainer ( #3318 )
2023-04-11 09:54:59 +08:00
a7ca297281
[coati] Fix LlamaCritic ( #3475 )
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* mv LlamaForCausalLM to LlamaModel
* rm unused imports
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Co-authored-by: gongenlei <gongenlei@baidu.com>
2023-04-07 11:39:09 +08:00
b09adff724
[chat]fix sft training for bloom, gpt and opt ( #3418 )
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fix sft training for bloom, gpt and opt
2023-04-04 09:46:23 +08:00
30412866e0
[chatgpt] add pre-trained model RoBERTa for RLHF stage 2 & 3 ( #3223 )
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* Add RoBERTa for RLHF Stage 2 & 3 (test)
RoBERTa for RLHF Stage 2 & 3 (still in testing)
* Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)"
This reverts commit 06741d894d06741d894d
2023-04-03 10:11:03 +08:00
b0ce5a1032
[Coati] first commit ( #3283 )
2023-03-28 20:25:36 +08:00
RichardoLuo